Automated configuration of printer settings

ABSTRACT

A factory server receives part requests from customer devices and controls one or more manufacturing tools, such as 3D printers, to fabricate the requested parts. The factory server implements several features to streamline the process of fabricating parts using the manufacturing tools. For instance, the factory server can facilitate the design of a part by extracting features from the part request and identifying model files having those features. The factory server can also select an orientation in which to fabricate the part and determine print settings to use when fabricating the part. In addition, the factory server can implement a process to fabricate a three-dimensional part with a two-dimensional image applied to one or more of its external surfaces. Furthermore, the factory server can also generate a layout of multiple part instances on a build plate of a 3D printer so that multiple part instances can be fabricated at once.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.62/413,692, filed Oct. 27, 2016, which is hereby incorporated byreference in its entirety.

TECHNICAL FIELD

This disclosure relates generally to manufacturing systems and moreparticularly to automated configuration of settings for athree-dimensional printer.

BACKGROUND

Traditional fabrication methods use one or more reusable templatecomponents to shape many identical copies of a part. For example,injection molding forms plastic parts within a reusable mold thatdefines the exterior surface of each part. Although template componentsenable economical manufacturing of each part, the template componentitself is expensive to manufacture because it must be durable enough toshape many (often thousands) of parts. Thus, template components addoverhead costs to traditional forming methods.

To alleviate the overhead costs of traditional forming methods, additivemanufacturing methods may be used to form parts without templatecomponents. For example, three-dimensional (3D) printing techniques formparts by sequentially depositing the layers of a part. Additivemanufacturing methods can thus reduce overhead costs by obviating theneed for template components. However, additive manufacturing stillentails overhead costs such as labor to configure manufacturingsettings. In 3D printing, a part may be oriented in one of severaldirections that affect the number and size of printed layers. Whereparts are small enough for a 3D printer to print multiple copiessimultaneously, the part orientation also affects the number of copiesthe 3D printer may print at a time. Additionally, the part orientationinfluences the amount and shape of auxiliary material used to supportoverhanging surfaces of the part. These numerous interconnected factorsthat influence manufacturing settings necessitate skilled labor thatadds to overhead costs of additive manufacturing.

SUMMARY

Before a 3D printer can be used to fabricate a part based on a modelfile, an operator of the 3D printer typically has to perform severalmanual steps to prepare the part for printing. For instance, theoperator may manually inspect the part file to determine whether thepart can be printed in the orientation indicated by the coordinate axesin the model file, or if a different orientation should be used.Similarly, the operator may select printer settings, such as print speedand quality, layer thickness, and material type. When performed manuallyby a human operator, these preparation steps can be time consuming andcan substantially slow down the rate at which an automated manufacturingenvironment can fabricate parts. In addition, a human operator may makemistakes or select less-than-optimal orientations or printer settings.

Rather than having a human operator perform these preparation steps, afactory server performs a process that selects the orientation in whicha part will be printed. In particular, the factory server identifies oneor more candidate orientations for the part. If the factory serveridentifies a single candidate orientation, the factory server selectsthis orientation. If the factory server identifies multiple candidateorientations, the factory server also scores each candidate orientationbased on geometric attributes of the orientation and uses the scores toselect a candidate orientation. The process can also automaticallydetermine other printer settings, such as print speed and quality, layerthickness, and material type, based on the model file, the selectedorientation, and additional information about the part that is providedby the customer (e.g., the intended environment or use of the part).This process allows the factory server to fulfill part requests morerapidly and with less interaction from a factory operator.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example automated manufacturingenvironment, according to one embodiment.

FIG. 2 is a block diagram of an example factory server, according to oneembodiment.

FIGS. 3A-3D are example screenshots of a customer interface provided bythe factory server, according to one embodiment.

FIGS. 4A-4E are example screenshots of a factory interface provided bythe factory server, according to one embodiment.

FIG. 5 is a block diagram of a print request processor, according to oneembodiment.

FIG. 6 is a flow chart illustrating an example process formachine-assisted part design using automated interpretation of a designrequest, according to one embodiment.

FIG. 7 is a flow chart illustrating an example process for automatedconfiguration of printer settings for part printing, according to oneembodiment.

FIG. 8 is a flow chart illustrating an example process for fabricating athree-dimensional part with a two-dimensional image applied to a surfaceof the part, according to one embodiment.

FIG. 9 is a flow chart illustrating an example process for generating alayout of one or more part instances on a build plate, according to oneembodiment.

FIG. 10 is a block diagram illustrating a computer system 1000 uponwhich embodiments described herein may be implemented, according to oneembodiment.

DETAILED DESCRIPTION

System Environment

FIG. 1 is a block diagram of an example automated manufacturingenvironment 100, according to one embodiment. The automatedmanufacturing environment 100 contains entities including one or morecustomer devices 110, factory servers 120, factory clients 125, andprinters 130 interconnected by one or more networks 140. The one or morecustomer devices 110, factory servers 120, and factory clients 125 maybe implemented as computer systems, such as the computer system 1000shown in FIG. 10. The manufacturing environment 100 may include othermanufacturing tools in addition to or as an alternative to the one ormore printers 130. One or more of the entities (e.g., customer device110, factory client 125) may be omitted.

A customer uses a customer device 110 to interact with factory server120 through a customer interface 205. For example, the customerinterface 205 enables the customer to request parts and monitor theirstatus through fabrication and delivery. In various embodiments, thecustomer interface 205 may be implemented as a web-based interfaceprovided by the factory server 120 and accessible via a web browserapplication on the customer device 110, a user interface in a nativeapplication executing on the customer device 110, some combination ofthe two (e.g., a WebView object within a native application), or someother type of interface that is accessible on the customer device 110.The customer device 110 may be any computer able to present the customerinterface and accept customer inputs through the customer interface.Example customer devices 110 include a desktop computer, a laptopcomputer, a tablet computer, or a mobile device (e.g., a smartphone). Acustomer device may present the customer interface through, for example,a display such as a screen or projector and/or through an audio devicesuch as speakers or headphones. A customer device 110 may accept inputsthrough one or more input devices such as, for example, a keyboard,mouse, pointer, touch screen, or microphone.

A factory server 120 controls one or more printers 130, othermanufacturing tools, or both. The factory server 120 provides one ormore interfaces enabling initiation, monitoring, and modification of themanufacturing process. For example, the factory server 120 provides acustomer interface for presentation to customers by customer device 110,a factory interface for presentation to operators by factory client 125,or both.

A customer interacts with the customer interface to complete a partrequest specifying attributes of one or more parts to be manufactured.As referred to herein, a part request is a request submitted by acustomer In one example implementation, a part request includes a threedimensional (3D) model file of the part, model scale information (e.g.,a distance unit), customer contact information, a requested deadline, apart material, a part color, and a quantity of the part. As anotherexample, a part request further includes an image to be fixed on asurface of the finished part. As a third example, the part request omitsa model file and instead includes descriptive information, such as awritten description, images, videos, or a combination thereof, depictingthe customer's conception of a requested part. In this third example,the part request may alternatively be referred to as a design request,and a factory operator such as a designer or artist may review thedesign request and convert the descriptive information of the part intoa model file for use in manufacturing.

The factory server 120 converts model files specifying a part'sgeometry, which are irrespective of manufacturing tool, intomanufacturing instructions usable by a particular type of manufacturingtool (e.g., printer 130) to fabricate the part. The model file may be athree-dimensional (3D) model specifying the geometrical layout of a partusing, for example, a mesh, surfaces, vectors, or a combination thereof.The manufacturing instructions (e.g., G-code) are readable by themanufacturing tool (or an operator thereof) and are specified in termsof elementary operations to at least partially fabricate the part. Inthe example of 3D printing, the manufacturing instructions specifypositions for a tool head to extrude material for sequentiallyfabricating a part's layers.

Conversion of the model file to manufacturing instructions depends atleast in part on manufacturing settings. Manufacturing settings dependon the type of manufacturing tool (e.g., the type of printer 130) andmay be specified at least in part by a customer's part request. Forexample, the part request specifies one or more colors for the part aswell as a material (e.g., plastic, rubber, metal) for the part. Thefactory server 120 may configure manufacturing settings formanufacturing a part specified by a model file. Configuringmanufacturing settings includes the factory server 120 determiningtool-specific manufacturing settings that correspond to tool propertiesof a particular manufacturing tool. For example, the factory server 120uses the tool properties of printer 130 such as build volume, buildvolume dimensions, build plate dimensions, or print resolution todetermine tool-specific manufacturing settings such as print layerthickness, print orientation, or plate layout. The factory server 120 isdescribed in further detail with respect to FIG. 2.

A factory operator uses factory client 125 to interact with factoryserver 120 through a factory interface. For example, the factoryinterface enables the factory operator to review order status, configuremanufacturing settings (e.g., part orientation, print layout on buildplate), monitor the status of a manufacturing tool (e.g., printer 130),generate or modify a design file to fulfill a customer request, or acombination thereof. The factory client 125 may be a computer devicesimilar to those described with respect to the customer device 110.

The printer 130 is a manufacturing tool that fabricates a part usingadditive manufacturing techniques such as fused deposition modeling,stereolithography, laser sintering, or laser melting. The printer mayfabricate a part using one or more materials including plastic, rubber,metal, other materials, or a combination thereof. An example printer 130includes a build plate that supports the base (e.g., top, bottom, side);one or more directional tools to control formation of the part; and anactuator to move the build plate relative to the directional tools orvice versa. For example, in fused deposition modeling, the directionaltool includes a nozzle that extrudes material onto a portion of thepart. In stereolithography, an example directional tool includes a laserand one or more mirrors directing the laser onto a portion of the part.Another example directional tool in stereolithography is a projectorforming a variable pattern on the part. The printer 130 usesmanufacturing instructions from factory server 120 to fabricate one ormore parts.

The manufacturing environment 100 may include other manufacturing toolscommunicatively coupled to the factory server 120. For example, suchmanufacturing tools include surface printers or subtractivemanufacturing tools (e.g., computer numerical control (CNC) lathes,mills, saws, or laser cutters).

Factory Server

FIG. 2 is a block diagram of an example factory server 120, according toone embodiment. The factory server 120 may include a non-transitory,computer-readable storage medium (i.e., a memory) storing the computerprogram instructions and one or more processors for executing thecomputer program instructions to cause the factory server 120 to performfunctionality associated with the program modules. For example, thememory includes one or more read-access memories, flash memories, orhard disk memories, or a combination thereof; and the processor includesone or more central processing units (CPUs), graphics processing units(GPUs), application-specific integrated circuits (ASICs), or acombination thereof. The memory may include other technologies, such asa quantum memory storing photonic quantum information. The factoryserver 120 may include one or more servers operated at one or morelocations. In some embodiments, the factory server 120 uses cloudcomputing services.

The factory server 120 includes computer program instructions forprogram modules including a customer interface 205, an applicationprogramming interface (API) 210, a print request processor 215, afactory interface 220, a factory monitor 225, a printer controller 230,a design tool 235, and a model file library 240. The factory server 120may include fewer or additional modules performing differentfunctionality, or a different combination of modules to perform similarfunctionality. In some implementations, the factory server 120 includesmultiple servers or other computers performing different subsets of thefunctionality described with respect to the example factory server 120.

The customer interface 205 enables a customer to place part requestswith the factory server 120. The customer interface 205 presents fieldsto request parameters of the part request from a user, which thecustomer interface 205 sends to the factory server 120 through API 210.In some implementations, a customer may directly interact with thefactory server 120 through the API 210 without using the customerinterface. Additionally, the customer interface 205 presents information(e.g., estimated part price, estimated completion date) queried from thefactory server 120 using the API 210. In some embodiments, the customerinterface is a web page containing instructions for a browser of thecustomer device 110 to present the customer interface. In someembodiments, the customer interface is an application native to anoperating system of the customer device 110.

In one configuration, the customer interface 205 offers two modes forcustomers to place part requests: (1) direct print and (2) quotationrequest. An example of such a configuration is illustrated in FIG. 3A,which presents a customer with a first option 302 to upload a file fordirect printing and a second option 304 to request a quote for aproject.

In the direct print mode, the customer interface 205 accepts requestparameters including one or more of customer contact information, amodel file, model file scale (e.g., distance units), part quantity, partcolor, and part material. The customer interface 205 queries the factoryserver 120 for an order price based at least in part on the estimatedamount of material used, expected print time, number of parts, or acombination thereof. The amount of material used may be estimated frompart surface area, part volume, dimensions of a rectangular prismbounding the part, print resolution, other print settings, or acombination thereof. The order price is presented to the customer in thecustomer interface. The factory server 120 may select manufacturingtools to complete the order, determine manufacturing settings, generatemanufacturing instructions, and initiate fabrication of the part usingthe selected manufacturing tools. In some instances, this process may becompleted without intervention by a factory operator. FIG. 3Billustrates the customer interface 205 in direct print mode beforerequest parameters are submitted to the factory server 120. FIG. 3Cillustrates the customer interface 205 in direct print mode after thefactory server 120 sends the estimated order price to the customerdevice 110.

In the quotation request mode, the customer interface 205 accepts one ormore of the request parameters used in direct print mode as well ascustom instructions. However, the factory server 120 generally holdsfulfilment of such a part request until further consultation with thecustomer to confirm order price, delivery date, or the custominstructions. FIG. 3D illustrates the customer interface in quotationrequest mode.

In one embodiment, the customer interface 205 facilitates“five-dimensional (5D) printing,” where a 3D part is fabricated andfinished by applying one or more images (e.g., two-dimensional (2D)images) to a surface of the 3D part. Using the customer interface 205,the customer may upload a model file of the part as described above, andfurther upload one or more images for application onto the part. Thecustomer interface 205 may enable the user to orient and place the imageon a graphical model of the part generated based on the part file.Production of the “5D part” is described further with respect to FIG. 8.

The print request processor 215 determines print settings and isdescribed further with respect to FIG. 5.

The factory interface 220 includes tools for factory operators to (1)monitor order status, as illustrated in FIG. 4A; (2) view order details,as illustrated in FIG. 4B; (3) configure printing parameters includingpart orientation and part layout on the build plate, as illustrated inFIG. 4C and FIG. 4D; and (4) configure other manufacturing settings, asillustrated in FIG. 4E.

An example factory interface 220 illustrating order status indicates,for each order, a receipt time, an order deadline, an order type (e.g.,direct print or quotation request (aka a “volume order), a project name(e.g., part name), a customer name, an order status, and orderstatistics (e.g., number of model files, number of parts, estimatedprint time). The order status refers to the stage of the object in themanufacturing process. Example order statuses include: in preparation(i.e., awaiting completion of orientation selection and layoutcompletion), printing, post-processing (e.g., harvesting from theprinter build plate, removing support material, painting, buffing,electroplating, other non-printing steps); and stalled (e.g., delayedfor a higher priority order or pending customer correction of an invaliddesign file). An order may be associated with multiple model files wherethe order corresponds to an object made of multiple parts eachcorresponding to one of the model files. For example, the ordercorresponds to an object larger than the printing capacity of a printer130, so the object is fabricated by printing multiple interlockingparts. Determination of estimated print or manufacturing time isdescribed further with respect to FIG. 3. An example factory interface220 illustrating order status is illustrated in FIG. 4A.

An example factory interface 220 illustrating order details indicatesfurther details about a particular order. The order details include theinformation described with respect to order status as well as theshipping status (e.g., shipping carrier, delivery date, trackinginformation). The order details include information about each of themodel files included in the order. For each model file, the orderdetails include a quantity to print (i.e., total quantity, or perinstance of the order quantity), number completed, fabrication material(e.g., PLA (polylactic acid) plastic), color, and print time remaining.A factory interface 220 presenting order status is illustrated in FIG.4B.

An example factory interface 220 for configuring printing parametersincludes a three-dimensional model of the part layout on the buildplate. The build plate may be represented by a plane with a grid, andthe available print volume may be represented by a wire frame box (orother applicable shape), as illustrated in FIG. 4C. In one embodiment,the layout planner 515 determines the layout of part instances on thebuild plate. An example of an automated process for generating a layoutof one or more part instances on a build plate is described with respectto FIG. 9. In this embodiment, the generated layout may be presented inthe factory interface 220 for review and approval by a factory operatorbefore the layout is printed.

In another embodiment, a factory operator interacts with the factoryinterface 220 to manually generate the layout of part instances on thebuild plate. In this embodiment, the factory operator selectsorientations for part instances from part files and places the partinstances on the build plate. The factory operator may place multiplepart instances from the same model file, part instances from multiplemodel files, or both. The interface includes part statistics about theeach model file, such as the volume and surface area of each mode file.The interface may include a slicer tool, which generates slices of theoriented part instance. The slices each correspond to a layer that maybe printed. Generally, the slices are oriented parallel to the plane ofthe build plate, but other configurations are possible. The slices areused to generate manufacturing instructions (e.g., G-code) that themanufacturing tool uses to manufacture the part.

An example factory interface 220 for configuration of printingparameters is illustrated in FIG. 4C and FIG. 4D. FIG. 4C illustratesthe factory interface before the part instances are oriented and placedon the build plate, and FIG. 4D illustrates the layout of the buildplate after the part instances are oriented and placed. The interfacemay include other orientation and layout options (e.g., part contactwith a part fixture of a CNC tool), depending on the manufacturing tool.

An example factory interface 220 for configuring other manufacturingsettings includes settings specific to the manufacturing tool (e.g.,printer 130). For a printer 130, the device settings may include anextruder temperature (i.e., temperature of plastic extruded from theprint head onto the build plate and part layers); a printer head travelspeed in the lateral direction (parallel to the build plate); a printerhead travel speed in the vertical direction (perpendicular to the buildplate); a fan speed (for cooling the printhead and printed part layers);and a minimum amount of time per layer (e.g., to ensure sufficientcooling of an extruded layer before application of a subsequent layer).Other manufacturing settings are possible depending on the manufacturingtool. An example factory interface 220 for configuration of othermanufacturing settings is illustrated in FIG. 4E.

The factory monitor 225 determines the tool status of printers 130 andother manufacturing tools. The status of a printer 130 may include timeremaining to complete a part (e.g., based on number of layers or volumecompleted out of total volume or layers); tool errors; or materialremaining (e.g., length of plastic material remaining in spool, color ofplastic material loaded).

The printer controller 230 controls printers 130 and other manufacturingtools based on the tool status collected by the factory monitor 225 andinstructions from factory operators. The printer controller 230 mayselect printers 130 and manufacturing tools to complete parts based onthe tool status determined by the factory monitor 225. For example, theprinter controller 230 selects printers 130 based on availability (e.g.,status as idle or printing), time until availability (based on timeremaining on a printing part and turnaround time), or both. As anotherexample, the printer controller 230 selects printers based on material(or material color) supplied to a printer 130 as well as material (ormaterial color) specified in a part request. The printer controller 230sends the selected printers 130 (or other manufacturing tools)manufacturing instructions to initiate fabrication. For example, themanufacturing instructions are G-code generated based at least in parton orientation and layout determined by the request processor 215, thefactory interface 220, or a combination thereof.

The design tool 235 receives a design request through the customerinterface 220 and presents the design request to a designer (using afactory client 125) who prepares a model file to fulfill the designrequest. The design tool 235 facilitates design by automaticallyextracting features from the design request and identifying images andmodel files having these features. Identified images or design files maybe presented to a customer, who can select one or more of the presentedimages or model files. Images or design files selected by the customer(or identified by the design tool 235) are presented to the designer,thereby reducing designer time to prepare a model file to fulfill thedesign request. The design tool 235 is described further with respect toFIG. 6.

The model file library 240 contains 3D model files of parts and,optionally, image files of fabricated parts. The 3D model files (andimage files) are tagged with features. The design tool 235 queries themodel file library using features to identify 3D model files, imagefiles, or both that are relevant to a design request.

Print Request Processing

FIG. 5 is a block diagram of a print request processor 215, according toone embodiment. The print request processor 215 includes a filevalidator 505, an orientation selector 510, a layout planner 515, aquote generator 520, a machine settings module 525, and an instructiongenerator 530.

The file validator 505 verifies that the model file can be fabricated.For example, the file validator 505 determines whether the file includessurfaces with inverted normals facing an opposite direction from normalsof other surfaces of the part. In general, surface normals face outward,so an inverted normal facing the opposite direction from adjacentsurface represents a topologically inconsistent part model. If the modelfile has inverted surface normals, the file validator 505 generates anerror message for presentation through the customer interface or factoryinterface. As another example, the file validator 505 determines whetherthe model file includes a “watertight” part having interconnectedexternal surface. A non-watertight part may be underdefined (e.g.,indeterminate thickness) or have undefined connections between portionsof the part. As a third example, the file validator 505 determines aminimum feature size of the part and determines whether the minimumfeature size is greater than feature size thresholds associated with thematerial included in the part request, the printers 130 (or othermanufacturing tools) of the factory, or both. As a fourth example, thefile validator 505 determines whether the part file includes aninsufficiently thick walls thinner than a thickness threshold of thematerial. As a fifth example, the file validator 505 determines whetherthe part file includes an insufficiently small joint area betweenportions of a part having less than a threshold area of the material. Asa sixth example, the file validator 505 estimates a quality measure forthe printed part (e.g., print resolution, surface roughness, surfaceflatness, edge straightness) and compares it to a threshold qualitymeasure to determine whether the printed part will attain sufficientquality. The threshold quality measure may be included in the partrequest or may be specified by a factory operator (e.g., as part of a“low,” “medium,” or “high” quality standard). If the model file includesan inverted surface normal, a non-watertight part, a part with anincompatible minimum feature size, an insufficient wall thickness, aninsufficient joint area, fails to attain sufficient quality, or acombination thereof, the file validator 505 generates an error messagefor presentation through the customer interface or factory interface.

The orientation selector 510 determines an orientation for fabricatingthe part relative to the build plate. Typically, a model file include athree-axis coordinate system, so the orientation selector 510 selects anorientation parallel or anti-parallel to one of the axes. However, theorientation selector 510 may also determine an off-axis orientation. Theorientation selector 510 may include an orientation display toolincorporated into the factory interface 220. Using the orientationdisplay tool, a factory operator may select an orientation for a part,review an orientation automatically determined by the orientationselector 510, or modify an automatically determined orientation.

The orientation selector 510 may select an orientation based on toolproperties of the printer 130. For example, a printer 130 has a definedbuild volume, and the orientation selector 510 selects an orientationthat ensures that each dimension of the part fits within the dimensionsof the print volume. As another example, the orientation selector 510selects an orientation based at least in part on a projected area of thepart on the build plate. The orientation selector 510 may select anorientation that minimizes the projected area to maximize the number ofinstances of the part that the printer 130 can print simultaneously. Theorientation selector 510 may select an orientation that maximizes theprojected area to minimize the number of layers orthogonal to the buildplate and hence to minimize the print time of the part. The orientationselector 510 may select an orientation that corresponds to anintermediate projected area between the minimum and maximum projectedareas to minimize a cost function based at least in part on print timeand number of build plates used to fulfill a print request. The numberof build plates to fulfill a print request depends on the number of partinstances printed on a build plate, as determined by the layout planner515.

The orientation selector 510 may select an orientation according to amachine learning model that uses features including one or more ofprojected area, part dimensions, another quantification of the part'sgeometry, number of part instances per build plate, manufacturing time,and tool properties, as described further with respect to FIG. 7. Theorientation selector 510 may select an orientation based at least inpart on a suggested orientation determined from the print request orinput by a factory operator. The orientation selector 510 may obtainmechanical requirements corresponding to the use of the part and selectan orientation to ensure that the printed part complies with themechanical requirements. This occurs because printed parts may haveanisotropic mechanical properties corresponding to the printorientation.

The layout planner 515 generates a layout for printing one or moreinstances of one or more parts on a build plate given the orientationsof the one or more part instances determined by the orientation selector510. The layout planner 515 may determine a footprint of each partinstance based on the projected area of the part onto the build plate.The part footprint may include a buffer zone around the projected areadepending on the tool properties of the printer 130. The buffer zoneneed not be based on projected area. For example, the buffer zone may bemodeled three-dimensionally to enable build plate layouts where partshave overlapping projected areas. The layout planner 515 generates alayout of the part footprints that substantially maximizes the number ofpart instances having part footprints completely within the boundary ofthe build plate. For example, the layout planner 515 uses atwo-dimensional bin packing algorithm. The layout planner 515 need notgenerate the optimal layout due to computational constraints anddiminishing returns of optimization. An example process for generating alayout of part instances on a build plate, which may be performed atleast in part by the layout planner 515, is described in further detailwith respect to FIG. 9.

The quote generator 520 determines an overall cost for a part requestbased on expected costs of printing the model file. The quote generator520 may determine the overall cost based on an anticipated manufacturingtime given the orientation determined for the part, part geometry, partfeatures, and tool properties (e.g., estimated cleaning time) of theprinter 130 or other manufacturing tools. The manufacturing time mayrefer to the total machine time to print the part instances in theorder, the minimum time to fulfill the order given a number ofmanufacturing tools available (and allocated) to fulfill the order, or acombination thereof. The quote generator 520 may determine the overallcost based on a number of build plates to fulfill a part request, whichdepends on the number of part instances in the print layout.

The machine settings module 525 determines other printer settings suchas tool head travel speed, manufacturing tool temperature, coolingequipment settings (e.g., for a fan), part infill, layer height, numberof outer shells, extrusion speed, other settings (e.g., supportsettings, raft settings), or any combination thereof.

The instruction generator 530 generates manufacturing instructions(e.g., G-code) for a manufacturing tool based at least in part on theorientation and layout determined for the build plate as well as othermachine settings. When used with additive manufacturing, the instructiongenerator 530 determines layers by slicing the part instances in adirection parallel to the plane of the build plate.

Machine-Assisted Part Design

In some cases, a customer may wish to have a part manufactured but doesnot have the ability to create a 3D model file for the part. Forexample, the customer might not have access to a computer-aided design(CAD) application suitable for create a 3D model file, or the customermight not have the time or skill needed to create a 3D model file thatproperly captures the desired structure, shape, and appearance of thepart that the customer wishes to have manufactured.

One possible solution is for the customer to provide a description oftheir desired part to another user (referred to herein as a designer) sothat the designer can create a 3D model file in a computer-aided design(CAD) application based on the descriptive information. However, thisprocess can be time-consuming for both the designer and the customer,especially when the process is performed at scale and a single designeris creating 3D model files for several customers. For instance, thecustomer and the designer may have to engage in several rounds ofcommunication before the designer has a proper understanding of thecustomer's desired part, and it may take the designer a substantialamount of time to create a new 3D model file from scratch based thedescription provided by the customer.

Rather than having a designer create a new 3D model file from scratch,the factory server 120 implements a process that interprets a designrequest received from a customer and selects one or more 3D model filesfrom a model file library that are similar to the customer's designrequest. The selected model files are then presented to the designer,and the designer can either copy portions of the model files or use themodel files as a reference when creating a new 3D model file to fulfillthe customer's design request. In some embodiments, the factory server120 first identifies a plurality of candidate model files and promptsthe customer to select the candidate model files that most accuratelyrepresent the customer's desired part. Selecting and providing modelfiles to the designer in this manner allows the designer to fulfill thecustomer's design request in less time and with greater accuracy.

FIG. 6 is a flow chart of an example process 600 for machine-assistedpart design using automated interpretation of a design request,according to one embodiment. In some implementations, the process 600includes a different order of steps, additional steps, or fewer steps.The process 600 described with respect to FIG. 6 may be performed by,for example, the design tool 235.

The factory server 120 receives 610 a design request from the customerinterface 205 presented by the customer device 110. As referred toherein, a design request is a type of part request that includesdescriptive information describing the desired properties of a partrequested by the customer (e.g., the desired structure, shape, and/orappearance of the part). For example, the descriptive information mayinclude a written description of the requested part, one or more imagesof the requested part, other media depicting the requested part, or acombination thereof. The descriptive information may also includeinformation in other formats. For example, the descriptive informationmay include an audio description of the requested part (which may be astandalone audio file or part of a video), and the factory server 120may use speech-to-text processing to derive a written description fromthe audio description. As another example, the descriptive informationmay include a video or animation (e.g., depicting multiple views of therequested part), and the factory server 120 may extract images fromframes of the video or animation.

The design request typically omits a model file for the requested part.However, in some embodiments the descriptive information may include oneor more model files in addition to the other types of descriptiveinformation described above. For example, the descriptive informationmay include a model file for a first portion of a requested part andfurther includes additional items of descriptive information thatdescribe the desired structure, shape, or appearance of a second portionof the requested. As another example, the descriptive informationincludes a model file for the requested part and further includesadditional items of descriptive information that describe one or moredesired changes that the customer wishes to have made to the model file.

The design request may additionally include other information describedabove as being included in a part request, such as customer contactinformation, a requested deadline, and an intended production quantityof the part.

The factory server 120 extracts 620 features from the descriptiveinformation. A feature represents a property extracted from thedescriptive information. Example features correspond to properties of apart such as a material, a color, a texture, an edge shape, a surfaceshape, a volumetric shape, or other aspects of a part. For example, thefactory server 120 uses natural language processing to identify 620keywords or phrases (e.g., “beaver,” “serrated,” “donkey,” “threadedhole”) in the design request. As used herein, a “keyword” may refer to asingle word, a phrase comprising multiple words, an acronym, or acombination thereof. As another example, the factory server 120 usesimage processing techniques such as edge detection or image segmentationto identify 620 one or more structures within the image. Each of thesestructures may be mapped to one or more keywords using a machinelearning algorithm (e.g., a decision tree, a neural network, a logisticclassifier). The extracted features may be binary (e.g., a binary valueindicating whether the property represented by the feature is present orabsent) or probabilistic (e.g., a scalar value representing theprobability that the descriptive information contains the propertyrepresented by the feature).

The factory server 120 may optionally use the extracted features toidentify 630 candidate images or models to present 640 to the customerin order to receive feedback. The factory server 120 uses the feedbackto refine the extracted features. The extracted features may also beused to select 660 model files to facilitate fulfilment of the designrequest.

The factory server 120 optionally identifies 630 candidate images ormodel files based on the features. For example, the factory server 120queries the model file library 240, which contains model files taggedwith various features. The model files may be manually tagged withfeatures by a designer, automatically tagged using a machine learningalgorithm, or a combination thereof. Similar to the extracted features,the features tagged for each of the model files may be binary values orscalar values.

The factory server 120 queries the model file library 240 to identifyand select model files tagged with features similar to the featuresextracted from the descriptive information. More specifically, thefactory server 120 determines a measure of similarity for one or moremodel files in the model file library 240 and selects one or more modelfiles based on their respective measures of similarity. In an embodimentwhere the extracted features are scalar values, the factory server 120may determine a measure of similarity for one or more model files in themodel file library 240 by comparing a feature vector containing taggedfeatures for the one or more model files with a feature vectorcontaining the extracted features. For example, the factory server 120may compute, for each of the one or more model files, a cosinesimilarity between the feature vector for the model file and the featurevector containing the extracted features. In this embodiment, thefactory server 120 may select model files having a measure of similaritygreater than a threshold value or rank the model files according totheir respective measures of similarity and select model files having aranking higher than a threshold ranking.

In another embodiment, the factory server 120 determines the measure ofsimilarity for a model file in the library 240 by determining a numberof features in common between the extracted features and the featurestagged for the model file. In this embodiment, the factory server 130may select a model file in response to the model file being tagged withat least a threshold number of the features extracted from thedescriptive information.

Instead of (or in addition to) using the features to identify 630candidate model files, the factory server 120 may identify 630 candidateimages from an image library, which may contain images of completedparts or images derived from views of model files. The model filelibrary 240 and image library may include model files and images fromexternal sources (e.g., a third-party database, a search engine).

The factory server 120 optionally presents 640 the identified images ormodel files through the customer interface 205 of the customer device110. The customer may select one or more of the identified images ormodel files to indicate that they are at least similar to the customer'sintended design. The customer interface 205 may present 640 multiplesets of images or model files, where each set corresponds to a differentproperty (e.g., color shade, overall shape, texture). The sets may beidentified by clustering the identified images or model files based on avector of the features and selecting a set of images or model files fromeach cluster. The customer device 110 sends the customer's selections(and non-selections) to the factory server 120.

The factory server 120 optionally modifies 650 the extracted featuresusing the customer's feedback on the presented images. The features maybe modified based on a weighted combination between the initial featuresand features of the presented images and model files. Images and modelfiles that the customer selects receive a positive weight, and thosethat the customer does not select are given a negative weight. Themodified features may be used to further identify 630 images and modelfiles to present 640 to the customer for feedback in order to furtherrefine the features associated with the design request.

The factory server 120 uses the features of the design request to select660 one or more model files or images to present to a designer. Thefactory server 120 may use features extracted 620 from the designrequest or the features modified 650 in response to customer feedback.The selection process is similar to identifying candidate images ormodel files, as described above with respect to step 630. For example,the factory server 120 determines a measure of similarity for one ormore model files in the model file library 240 and selects 660 one ormore model files based on their respective measures of similarity in themanner described above with respect to step 630. In various embodiments,the factory server 120 may determine the measure of similarity for theimages or model files based on, for example, a number of features incommon, a cosine similarity, or a combination thereof. However, thefactory server 120 may use different threshold values when selecting 660the model files to present to the designer versus identifying 630candidate model files (or images) to present to the customer. Forexample, the threshold values for these two steps may be configured sothat the factory server 120 identifies 630 a larger number of candidatemodel files to present to the user and selects 660 a smaller number ofmodel files to present to the designer.

The factory server 120 presents 670 the selected model files or imagesto the designer. In one embodiment, the selected model files or imagesare presented 670 to the designer using the factory client 125. Inanother embodiment, the selected model files or images are presented tothe designer at a separate computing device that is remote to thefactory server 120 and the factory client 125. For example, the separatecomputing device communicates with the factory server 120 over thenetwork 140. This allows the designer to be located in a differentgeographic location than the factory server 120 and the printer 120.

The designer fulfills the design request using the model files or imagesin conjunction with the descriptive information received from thecustomer as part of the design request. For example, the designer mayrefer to the selected model files or images to design a model file thatis consistent with the descriptive information. The designer may alsocopy portions of a selected model file to reduce time for creating thenew model file. In one embodiment, the factory client 125 (or theseparate computing device) also presents a user interface that allowsthe designer to design the model file that fulfills the design request.For example, a CAD application is installed on the factory client 125and the factory client 125 presents a user interface of the CADapplication. After the designer fulfills the design request, theresulting model file may be sent to the factory server 120, and thefactory server 120 may generate manufacturing instructions (e.g.,G-code) for the model file and control one of the printers 130 tofabricate the model file. The factory server 120 may also send the modelfile to the customer device so that the customer can keep the model filefor their records.

Automated Configuration of Printer Settings

Before a 3D printer can be used to fabricate a part based on a modelfile, an operator of the 3D printer typically has to perform severalmanual steps to prepare the part for printing. For instance, theoperator may manually inspect the part file to determine whether thepart can be printed in the orientation indicated by the coordinate axesin the model file, or if a different orientation should be used.Similarly, the operator may select printer settings, such as print speedand quality, layer thickness, and material type. When performed manuallyby a human operator, these preparation steps can be time consuming andcan substantially slow down the rate at which the automatedmanufacturing environment 100 can fabricate parts. In addition, a humanoperator may make mistakes or select less-than-optimal orientations orprinter settings.

Rather than having a human operator perform these preparation steps, thefactory server 120 performs a process that selects the orientation inwhich a part will be printed. In particular, the factory server 120identifies one or more candidate orientations for the part. If thefactory server 120 identifies a single candidate orientation, thefactory server 120 selects this orientation. If the factory server 120identifies multiple candidate orientations, the factory server 120 alsoscores each candidate orientation based on geometric attributes of theorientation and uses the scores to select a candidate orientation. Theprocess can also automatically determine other printer settings, such asprint speed and quality, layer thickness, and material type, based onthe model file, the selected orientation, and additional informationabout the part that is provided by the customer (e.g., the intendedenvironment or use of the part). This process allows the factory server120 to fulfill part requests more rapidly and with less interaction froma factory operator.

FIG. 7 is a flow chart of an example process 700 for automatedconfiguration of printer settings for part printing, according to oneembodiment. In some implementations, the process 700 includes adifferent order of steps, additional steps, or fewer steps.

The factory server 120 obtains 710 a model file for a part. For example,the customer interacts with the customer interface 205 to upload a modelfile. In addition to obtaining 710 the model file, the factor server 120may also obtain additional information about the part from the customer.For example, the customer may provide (e.g., via the customer interface205) information about the part's intended environment (e.g., whetherthe part will frequently be subjected to vibrations or mechanical loads,the expected position and direction of the mechanical loads, and whetherthe part will be exposed to particular temperature ranges or thermalcycling). The customer may also provide information about the part'sintended use, or simply indicate a desire for the part to be fabricatedin a manner that yields certain material properties, such as increasedstiffness, increased flexibility, or increased/decreased thermalexpansion.

After obtaining 710 the model file, the factory server 120 generates 720a feature vector based on the model file. Each value in the featurevector represents a geometric feature of the part. For example, thefeature vector may include values representing orientations and sizes ofexternal flat surfaces on the part. As another example, the featurevector may include the length, width, and height of the part along thecoordinate axes in the model file. As a third example, the featurevector may include values representing the projected areas of the partalong the three coordinate plates in the model file. As a fourthexample, the feature vector may include values representing the flatnessor curvature of surfaces perpendicular to each of the coordinate axis.As a fifth example, the feature vector may include values representingthe orientation of intricate features, such as raised or depressedlettering or logos. The factory server 120 may identify such intricatefeatures from regions having locally high curvature or having arelatively high amount of information represented in high-frequencycomponents of a spatial Fourier transform or other similar transform ofthe model.

The factory server 120 identifies 730 one or more candidate orientationsfor the part based on the feature vector for the part. Each candidateorientation represents a possible way of orienting the part on a buildplate of a 3D printer. In one embodiment, a candidate orientation isdefined as a normal vector that represents the direction perpendicularto the surface of the build plate. For example, a candidate orientationmay correspond to one of the coordinate axes included in the model file,in which case the coordinate axis (e.g., the z-axis) is the normalvector. However, the factory server 120 may also identify othercandidate orientations for the part. In one embodiment, the factoryserver 120 provides the feature vector that was generated 720 from themodel file to a first machine learning model, and the machine learningmodel outputs one or more candidate orientations. The first machinelearning model may be trained with feedback received from a factoryoperator, as described further with reference to step 750. In variousembodiments, the first machine learning model may be a statisticaldecision tree, a neural network, or a combination thereof.

In another embodiment, the factory server 120 performs a heuristicanalysis of the feature vector to identify 730 one or more candidateorientations for the part. For example, the factory server 120 mayidentify the coordinate plane with the largest projected area andidentify a candidate orientation defined by a normal vector that isperpendicular to this coordinate plane. As another example, the factoryserver 120 may identify a candidate orientation in which an intricatefeature on the part is parallel to the build plate (i.e., the normalvector of the candidate orientation is substantially perpendicular tothe intricate feature). This allows the part to be printed with theintricate feature facing away from the build plate (i.e., with the sideof the part opposite to the intricate feature resting on the buildplate), which may reduce failure rates when fabricating the intricatefeature on the part.

In still another embodiment, the factory server 120 identifies 730 oneor more candidate orientations for the part by performing a heuristicanalysis on the model file without generating 720 a feature vector. Forexample, the factory server 120 identifies exterior flat surfaces thatare relatively large compared to other flat surfaces around the exteriorof the part or compared to the total external surface area of the part.In this example, the factory server 120 generates one or more candidateorientations defined by a normal vector perpendicular to the identifiedlarge flat surfaces. In other words, in each of these candidateorientations, an identified flat surface is parallel to the plate of thebuild plate, which allows the part to be printed with the flat surfaceresting on the build plate.

To identify the relatively large flat surfaces, the factory server 120may, for example, compute a total external surface area of the part,compute the surface area of a plurality of flat surfaces around theexterior of the part, and identify exterior flat surfaces having asurface area greater than a threshold percentage of the total externalsurface area. For instance, the factory server 120 may identify exteriorflat surfaces having a surface area greater than 10% of the totalexternal surface area of the part.

The factory server 120 generates a set of geometric attributes for eachof the candidate orientations and scores 740 each candidate orientationbased on its geometric attributes. Each of the geometric attributesquantifies one aspect of the part's geometry when the part is in aparticular candidate orientation. The geometric attributes may includethe dimensional properties of the part derived based on the identifiedcandidate orientations. For example, the geometric attributes for acandidate orientation may include the length and width of the part alongthe plane of the build plate. As another example, the geometricattributes for a candidate orientation may include the height of themodel when the part is resting on the build plate (i.e., the height ofthe model in the direction of the normal vector). As still anotherexample, the geometric attributes for a candidate orientation mayinclude the projected areas of the part onto the plane of the buildplate or onto a plane perpendicular to the build plate. As a furtherexample, the geometric attributes may include the flatness or curvatureof surfaces perpendicular or parallel to the build plate. As a fourthexample, generating the geometric attributes for a candidate orientationmay include calculating a volume or surface area of support material tobe added when fabricating the part in the candidate orientation.

The factory server 120 may also generate geometric attributes for acandidate orientation that represent the orientation of one or moreintricate features on the part when the part is placed in the candidateorientation. As noted above, the factory server 120 may identify suchintricate features based on a spatial Fourier transform or other similartransform of the model. A geometric attribute representing theorientation of one or more intricate features may have a more favorablevalue (i.e., a value that increases the likelihood of the candidateorientation being selected) when the intricate features are closer tobeing oriented parallel to the build plate. This is advantageous, forexample, because it may be easier for a 3D printer to fabricate anintricate feature when the feature is oriented substantially parallel tothe build plate.

The factory server 120 may also generate a geometric attributecorresponding to an estimated printing time for a part in a givencandidate orientation. The factory server 120 may also generate ageometric attribute for a candidate orientation corresponding to anestimate of how many copies of a part may be printed on a single buildtray when the part is printed in the candidate orientation.

The factory server 120 scores 740 the candidate orientations based onthe geometric attributes for each candidate orientation. The scoring ofthe candidate orientations may be performed based on a weightedcombination of the geometric attributes. The scoring may be based onweights determined by a second machine learning model (e.g.,statistical, decision tree, neural network, or a combination thereof).For example, the score for a candidate orientation may represent the“cost” or difficulty of printing the part using the candidateorientation. Alternatively, the score for a candidate orientation mayincrease with decreasing difficulty of printing the part using thecandidate orientation so that candidate orientations that allow for“easier” printing have higher scores. As other examples, the score for acandidate orientation may represent the quality, strength, and/orpotential failure rate of the part if the part is printed in thecandidate orientation.

The factory server 120 selects 750 one of the candidate orientationsbased on the scores. In one embodiment, the factory server 120 selects asingle “best” candidate orientation having the lowest “cost” score. Inthis embodiment, the factory server 120 may optionally present theselected candidate orientation to an operator using the factory client125, which prompts the operator to approve or reject the presentedcandidate orientation. If the operator rejects the presented candidateorientation, the operator may input a corrected orientation (e.g., byselecting one of the non-selected candidate orientations). If theoperator approves the presented candidate orientation, the presentedcandidate orientation is retained as the selected candidate orientation.

In another embodiment, the factory server 120 first selects a pluralityof candidate orientations according to the scores. For example, thefactory server 120 ranks the candidate orientations according to thescores and selects one or more candidate orientations based on theranking (e.g., the three lowest-difficulty orientations). Similar to theprevious embodiment, the factory server 120 may optionally present thepart according to each of the selected candidate orientations and promptthe operator to choose between of one of the presented candidateorientations. Alternatively, the operator may reject all of thepresented candidate orientations, in which case the operator may input adifferent orientation (e.g., one of the non-selected candidateorientations).

When the operator provides input (e.g., approval, rejection, orselection of a candidate orientation), the factory server 120 may usethe operator input for training the first machine learning model (whichmay be used to identify one or more candidate orientations), the secondmachine learning model (which may be used to score the candidateorientations), or some other algorithm that is used to determine weightsfor scoring the candidate orientations. Training data may also becollected from previous prints based on the orientations before andafter operator input.

In an alternative embodiment, the factory server 120 identifies 730 asingle candidate orientation based on the feature vector for the part.In this embodiment, the identified candidate orientation mayautomatically be selected 750. Alternatively, the factory server 120presents the identified candidate orientation to the factory operatorand prompt the operator to approve or reject the candidate orientationin the same manner as described above.

The factory server 120 determines 760 print settings based on theselected orientation and additional information about the part that isprovided by the customer (e.g., the intended use of the part). As notedabove, information about the intended environment of the part mayinclude, for example, information indicating whether the part will besubjected to mechanical loads, particular temperature ranges, thermalcycling, or vibration. Determined print settings may include print speedand quality, layer thickness, number of outlines, material type,printing temperature, rafts, type of support material, and infill. Forexample, if the additional information indicates that the part'sintended environment will subject the part to mechanical loads, thefactory server 120 may select printer settings that yield a higher partstrength (e.g., thicker layers, higher number of outlines, etc.). Asanother example, if the additional information indicates that the part'sintended use necessitates higher part quality (e.g., the part is meantto be presented as a gift or displayed prominently as a decorativepiece), then the factory server 120 may select printer settings thatyield a higher part quality (e.g., lower print speed, thinner layers,etc.).

In addition to determining 760 print settings based on the selectedorientation, the factory server 120 may also generate a layout of one ormore instances of the parts on the build plate, where the parts areoriented in the selected orientation. An example process for determininga layout of part instances on a build plate is described below withrespect to FIG. 9.

The factory server 120 instructs a printer 130 to print 770 the part inthe selected orientation and using the determined print settings. Forexample, the factory server 120 generates manufacturing instructions(e.g., G-code) and sends the manufacturing instructions to the printer130 along with the determined print settings.

Although the process 700 is described with reference to the factoryserver 120 of the automated manufacturing environment 100, this process700 for automatically orienting a part on a build plate andautomatically determining print setting may also be applied to othertypes of manufacturing environments in which a 3D printer is used. Forexample, the process 700 may be adapted for use with a 3D printersuitable for use (printing one part at a time or printing a relativelysmall number of parts at a time) by a user in a non-factory setting,such as a home, laboratory, or workplace. Continuing with this example,one or more candidate orientations may be presented to the user (i.e.,the same user who provides the model file) rather than to a factoryoperator in order to receive approval, rejection, or selection of acandidate orientation.

Fabrication of Three-Dimensional Part with Two-Dimensional Image AppliedThereon

In addition to manufacturing a three-dimensional part, in some instancesa customer may wish to apply a two-dimensional graphic or image to oneor more faces of the part. For example, the customer may wish to print acompany logo, an ornamental graphic, a graphic imitating a texture(e.g., wood or marble), or a color (e.g., colored headlights for a toycar) on the part. One possible process for fabricating a part with animage printed on one of its surfaces is to 3D print the part on a buildplate, physically remove the part from the build plate, reorient thepart so that the one or more faces with the printed image are accessibleto an image printer (e.g., a pad printer) and oriented correctlyrelative to the axes of the image printer, and then apply the 2D imageto the face. This process can be time consuming, especially whenperformed at a large scale, because the steps of removing the part fromthe build plate and reorienting the part are typically performed by ahuman operator.

Rather than having a human operator remove the 3D part from the buildplate and reorient the part prior to applying the 2D image, the factoryserver 120 implements a process that prepares the 3D part for printingso that the 3D part is printed in an orientation that allows the 2Dimage can be applied to the appropriate faces of the 3D part withouthaving to remove the part from the build plate. The factory server alsogenerates printing instructions for the 2D image, such as scaling androtating the image to account for the size of the part and theorientation of the part on the build plate. This results in a moreefficient and streamlined process for fabricating a 3D part with a 2Dimage printed on one or more of its faces.

FIG. 8 is a flow chart of an example process 800 for fabricating athree-dimensional part with a two-dimensional image applied to a surfaceof the part, according to one embodiment. In some implementations, theprocess 800 includes a different order of steps, additional steps, orfewer steps.

The factory server 120 obtains 810 a model file of a 3D part and animage file. Typically the image file is a two-dimensional image, butsome images may have additional dimensional components (e.g., imagesfrom a stereoscopic camera, holographic images, texture mapscorresponding to a three-dimensional mesh). In one embodiment, the 3Dpart and the 2D image are obtained 810 through the customer interface205. For example, the customer interacts with the customer interface 205to upload the model file and the image file.

The factory server 120 also obtains 810 a placement of the 2D image onthe 3D part. As referred to herein, a placement of a 2D image on a 3Dpart specifies one or more faces of the 3D part and a portion of each ofthe faces on which 2D image is to be printed. The faces on which the 2Dimage is to be printed are hereinafter referred to as the image faces.In one embodiment, the customer interface 205 allows the customer toplace the image on the part. Additionally or alternatively, a factoryoperator uses the factory interface 220 to place the image on the partbased on instructions from the customer. The customer interface 205 (orfactory interface 220) may present an estimated appearance of the imageon the part based on a color of the underlying part and the colors ofthe image. The interface 205 or 220 may include tools to modify thecolors of the image so that the printed image on the fabricated part hasthe appearance desired by the customer. For example, the tools includeslider bars to change the relative intensity of color pigments or tomodify the overall lightness or darkness of the image.

The factory server 120 prepares 820 the part for fabrication based onthe placement of the image on the part. In one embodiment, preparing 820the part for fabrication includes selecting an orientation for the partrelative to the build plate that facilitates easier printing of theimage on the image faces after the part is fabricated. In particular,the factory server 120 may select an orientation for fabricating thepart such that the one or more image faces are oriented facing away froma build plate (e.g., upwards). Orienting the part in this manner isadvantageous, for example, because it allows the image be printed on theimage faces of the part without removing the part from the build plate.This also allows the build plate to be used as a natural fixture to holdthe part in a known location when applying the 2D image.

In some implementations, the image may be printed on the part in a sideorientation, so the factory server 120 selects an orientation thatprovides at least a threshold amount of clearance between an edge of theimage and the build plate. For example, if the part is a triangularprism with images placed on one half of each of its rectangular faces,then the factory server 120 orients the rectangular faces perpendicularto the build plate such that the halves of the rectangular faces withthe images are farther from the build plate surface than the halves ofthe rectangular faces without the images. Orienting the part to providea threshold of clearance can also allow the image to be printed on theimage faces without removing the part from the build plate.

The factory server 120 determines other print settings based on theselected orientation. In some cases (e.g., where images are to beprinted on all sides of a part), the placement of the images does notaffect the selected orientation. The factory server 120 generatesprinting instructions for the printer 130 based on the determined printsettings.

In some embodiments, preparing 820 the part for fabrication based on theplacement of the image includes modifying the model file. For example,the factory server 120 modifies the model file to add a protrudingsurface corresponding to the placed image. As another example, thefactory server 120 modifies the model file to add a complimentarysurface texture, such as a rough texture or a wavy texture. Theprotruding surface may be embossed according the content of the image.For example, the factory server 120 identifies figures in the image(e.g., alphanumeric characters, faces, or other shapes) and generatespositive or negative relief matching the identified figures. Theidentified figures may be identified using optical characterrecognition, facial recognition, other object detection algorithms, edgedetection, other image processing techniques, user input, or acombination thereof.

The factory server 120 generates 830 2D printing instructions based onthe placement of the image on the part. Generating the 2D printinginstructions may include scaling and rotating the image based on theresolution of the printer and the area of the part selected for theimage. Generating the 2D printing instructions may further includegenerating colors for the printed image based on properties of theprinter, properties of the part material (e.g., color, roughness), orboth. Such color generation may automatically correct for colordistortions resulting from printing on a colored surface of the part.The color correction may further be based on expected lightingconditions of the part (e.g., outdoor use, indoor use in a dimly litroom, indoor use in a brightly lit room). The particular format of the2D printing instructions depends on the type of image printer. Forexample, the image printer may be a UV printer, an inkjet printer, or apad printer. If the image printer is a pad printer, generating the printinstructions may include selecting a pad type. For example, the printinstructions may specify a pad with a particular shape, size, stiffness,or material. As further examples, the print instructions may specify aparticular ink type or pad printer type.

The factory server 120 may optionally generate 840 one or more modelfiles to facilitate printing the image on the fabricated part. Forexample, the one or more model files include a fixture to hold thefabricated part in place during the image printing process. As anotherexample, the one or more model files include a stereotype or cliché fora pad printer to print the image. The model files (e.g., fixture, padstereotype) may be placed on a same build plate as one or more copies ofthe part. In this way, the steps of preparing the pad printer arepartially incorporated into fabricating the part itself, thus reducingmanufacturing time and costs.

The factory server 120 fabricates 850 the part based on the model file.Fabricating may include 3D printing, other additive manufacturingmethods, or other methods described herein used separately or incombination. Optionally, fabricating the part includes fabricating afixture to facilitating image printing, fabricating a stereotype for padprinting the image, or both. The factory server 120 prints 860 the imageon the part based on the 2D printing instructions. Printing the image860 may include using a fixture or stereotype fabricated with the part.Printing the image 860 may occur without using a fixture. For example,the image is printed while the part is attached to the build plate.

Automated Layout of Part Instances on Build Plate

In some implementations, the factory server 120 controls 3D printers 130that are large enough to print multiple part instances on a single buildplate. For example, the build plate may be large enough for the 3Dprinter to print thirty part instances at once (e.g., 30 copies of thesame part, single copies of 30 different parts, etc.). However, beforethe 3D printer can print the part instances, the part instances have tobe laid out at different, non-overlapping positions on the build plate.One method of laying out the part instances is simply to have a factoryoperator manually manipulate the part instances to create the layout,which can be time-consuming, inefficient, and suboptimal. Alternatively,the factory server 120 may perform a 3D packing process to placedifferent part instances on the build plate, but a 3D packing processcan be relatively computationally intensive.

Instead, the factory server 120 performs an automated layout process inwhich the factory server 120 generates a two-dimensional (2D) footprintfor each part instance and performs a two-dimensional packing algorithmto arrange the footprints on the build plate. The packing algorithmestablishes the position of each part instance within the layout. Aftergenerating the layout, the factory server 120 controls a 3D printer 130to fabricate the layout. This automated layout process is faster than ahuman operator, and using a two-dimensional packing algorithm makes theprocess more computationally efficient than a three-dimensional packingprocess.

FIG. 9 is a flow chart of an example process 900 for automated layout ofone or more part instances on a build plate, according to oneembodiment. In some implementations, the process 900 includes adifferent order of steps, additional steps, or fewer steps.

The factory server 120 obtains 910 one or more model files of parts tobe fabricated. In addition to obtaining the model files, the factoryserver 120 also obtains a number of instances of each part to fabricate.For example, the factory server 120 may obtain 910 instructions tofabricate three instances of a first part, four instances of a secondpart, and one instance of a third part. For ease of description, eachinstance of a part is referred to as a part instance. For instance,there are a total of eight part instances in the previous example. Inone embodiment, a customer uses the customer interface 205 to upload amodel file of a part and specify the number of instances of the partthat should be fabricated.

The factory server 120 may optionally define 920 a printable areacovering a portion of the build plate. In one embodiment, the printablearea covers a first portion of the build plate that includes the centerof the build plate and extends outward toward the edges, but stops shortof the edges. Instead, the factory server 120 creates a non-printablearea along the edges of the build plate. In other words, the printablearea covers the first portion of the build plate, and the non-printablearea covers a second portion of the build plate that has outer edgescorresponding to the edges of the build plate and inner edgescorresponding to the edges of the first portion.

It is advantageous to define 920 the printable area in this mannerbecause, for example, failure rates may be higher for part instancesthat are fabricated closer to an edge of the build plate (e.g., due towarping or dislodging of the part during fabrication because the buildplate is not perfectly level). Thus, by defining 920 a printable areathat stops short of the edges and defining a non-printable area alongthe edges, the factory server 120 generates a layout that does not placeany part instances within a threshold distance of an edge of the part,which reduces the overall failure rate of the part instances in thelayout.

In various embodiments, the non-printable region can have apredetermined width or a width based on the dimensions of the buildplate. For example, the factory server 120 may define a non-printableregion with a fixed width (e.g., 2 inches) on all four sides of thebuild plate. As another example, the factory server 120 may define anon-printable region whose width along each edge of the build plate is afixed percentage (e.g., 10%) of the build plate dimension in thedirection perpendicular to the edge. For instance, if the build platehas dimensions of 20 inches by 30 inches, the non-printable region has awidth of 3 inches (10% of 30 inches) along the two 20-inch sides and awidth of 2 inches (10% of 20 inches) along the two 30-inch sides. As aresult, the printable area has dimensions of 16 inches by 24 inches. Inan embodiment where this step 920 is omitted, the factory server 120uses the entire surface area of the build plate as the printable area.

The factory server 120 may optionally determine 930 an orientation foreach part to be printed. In one embodiment, the factory server 120determines 930 an orientation for a part by steps 720 through 750 of theprocess 700 described with respect to FIG. 7 for the model filecorresponding to the part. In another embodiment, the factory server 120performs a different process to determine 930 an orientation of thepart. For example, the factory server 120 automatically selects theorientation perpendicular to the largest exterior flat surface of thepart. In an embodiment where this step 930 is omitted, the factoryserver 120 may use one of the coordinate axes in the model file (e.g.,the z-axis) for a part as the orientation for the part. In an embodimentwhere this step 930 is included, the factory server 120 performs thesubsequent steps 940 through 970 of the process 900 based on thedetermined 930 orientation for each part.

The factory server 120 creates 940 a 2D footprint for each partinstance. As referred to herein, the 2D footprint for a part instance isa two-dimensional region that encompasses the portion of the build platethat the part instance will occupy while the part instance is beingfabricated. In one embodiment, the factory server 120 creates 940 a 2Dfootprint for a part instance by computing a projection of the partinstance onto the plane of the build plate. However, the 2D footprintfor a part may also have a different shape and be created 940 in adifferent manner. For instance, in another embodiment, the 2D footprintfor a part instance is a bounding rectangle that encompasses theprojection of the part instance onto the place of the build plate. Thebounding rectangle may be created, for example, by finding the x, y, andz dimensions of the smallest rectangular prism that fits the partinstance, and then using the x and y dimensions as the dimensions of thebounding rectangle. Using a bounding rectangle as the 2D footprint(rather than a projection of the part instance) can be advantageous, forexample, because the subsequent step of generating 950 a layout based onthe 2D footprints is both simpler and less computationally demanding ifthe 2D footprints are rectangular.

The factory server 120 may optionally expand the 2D footprint for a partinstance by adding a buffer zone around the 2D footprint. In anembodiment where 2D footprint is created 940 as a bounding rectangle,the factory server 120 may add the buffer zone by expanding the boundingrectangle by a fixed amount (e.g., 1 inch) in all four directions.Alternatively, the factory server 120 may add the buffer zone byidentifying a centroid of the 2D footprint that was created 940 andscaling the 2D footprint by a fixed percentage (e.g., 10%) about thecentroid.

Because different instances of the same part are identical, this step240 may be performed a single time for a part to create a 2D footprintfor the part (and optionally add a buffer zone around the 2D footprint),and the factory server 120 may then associate the resulting 2D footprintwith each instance of the part.

The factory server 120 generates 950 a layout of part instances on thebuild plate based on the 2D footprints for the part instances. In oneembodiment, the factory server 120 performs a two-dimensional binpacking algorithm or sprite packing algorithm to generate 950 thelayout. If the factory server 120 defined 920 a printable area of thebuild plate, the factory server 120 generates 950 a layout within theboundaries of the printable area. If the factory server 120 did notdefine 920 a printable area, the factory server 120 generates 950 alayout within the edges of the build plate.

The factory server 120 generates 960 manufacturing instructions (e.g.,G-code) for the generated 950 layout of part instances on the buildplate. In one embodiment, the factory server 120 generates a combinedmodel file representing the layout of the part instances aftergenerating 950 the layout. In this embodiment, the factory server 120generates 960 manufacturing instructions for the combined model file. Inanother embodiment, the factory server 120 generates separatemanufacturing instructions for each of the model files, and aftergenerating 950 the layout, the factory server 120 joins themanufacturing instructions to generate 960 the manufacturinginstructions for the layout.

After generating 960 the manufacturing instructions, the factory server120 controls a 3D printer 130 to print 970 the layout on a build platein order to fabricate the part instances in the layout. For example, thefactory server 120 sends the manufacturing instructions for the layoutto a 3D printer 130, and the 3D printer 130 executes the manufacturinginstructions to print 970 the layout.

Hardware Components

FIG. 10 is a block diagram illustrating a computer system 1000 uponwhich embodiments described herein may be implemented. For example, inthe context of FIG. 1, the customer device, the factory server 120, andthe factory client 125 may be implemented using the computer system 1000as described in FIG. 10. The customer device 120, the factory server120, and/or the factory client 125 may also be implemented using acombination of multiple computer systems 1000 as described in FIG. 10.The computer system 1000 may be, for example, a laptop computer, adesktop computer, a tablet computer, or a smartphone.

In one implementation, the system 1000 includes processing resources1001, main memory 1003, read only memory (ROM) 1005, storage device1007, and a communication interface 1009. The system 1000 includes atleast one processor 1001 for processing information and a main memory1003, such as a random access memory (RAM) or other dynamic storagedevice, for storing information and instructions to be executed by theprocessor 1001. Main memory 1003 also may be used for storing temporaryvariables or other intermediate information during execution ofinstructions to be executed by processor 1001. The system 1000 may alsoinclude ROM 1005 or other static storage device for storing staticinformation and instructions for processor 1001. The storage device1007, such as a magnetic disk or optical disk, is provided for storinginformation and instructions.

The communication interface 1009 can enable system 1000 to communicatewith one or more networks (e.g., the network 140) through use of thenetwork link (wireless or wireline). Using the network link, the system1000 can communicate with one or more computing devices, and one or moreservers. The system 1000 can also include a display device 1011, such asa cathode ray tube (CRT), an LCD monitor, or a television set, forexample, for displaying graphics and information to a user. An inputmechanism 1013, such as a keyboard that includes alphanumeric keys andother keys, can be coupled to the system 1000 for communicatinginformation and command selections to processor 1001. Othernon-limiting, illustrative examples of input mechanisms 1013 include amouse, a trackball, touch-sensitive screen, or cursor direction keys forcommunicating direction information and command selections to processor1001 and for controlling cursor movement on display device 1011.Additional examples of input mechanisms 1013 include a radio-frequencyidentification (RFID) reader, a barcode reader, a three-dimensionalscanner, and a three-dimensional camera.

Examples described herein are related to the use of the factory server100 for implementing the techniques described herein. According to oneembodiment, those techniques are performed by the system 1000 inresponse to processor 1001 executing one or more sequences of one ormore instructions contained in main memory 1003. Such instructions maybe read into main memory 1003 from another machine-readable medium, suchas storage device 1007. Execution of the sequences of instructionscontained in main memory 1003 causes processor 1001 to perform theprocess steps described herein. In alternative implementations,hard-wired circuitry may be used in place of or in combination withsoftware instructions to implement examples described herein. Thus, theexamples described are not limited to any specific combination ofhardware circuitry and software.

Additional Configuration Considerations

As used herein, the term “includes” followed by one or more elementsdoes not exclude the presence of one or more additional elements. Theterm “or” should be construed as a non-exclusive “or” (e.g., “A or B”may refer to “A,” “B,” or “A and B”) rather than an exclusive “or.” Thearticles “a” or “an” refer to one or more instances of the followingelement unless a single instance is clearly specified.

The drawings and written description describe example embodiments of thepresent disclosure and should not be construed as enumerating essentialfeatures of the present disclosure. The scope of the invention should beconstrued from any claims issuing in a patent containing thisdescription.

What is claimed is:
 1. A method comprising: obtaining a model file for apart to be fabricated by a three-dimensional (3D) printer; identifying aplurality of candidate orientations for the part using a machinelearning model, each of the candidate orientations representing anorientation of the part on a build plate of the 3D printer, the machinelearning model trained on a data set of candidate orientations for aplurality of model files that were previously printed, wherein the dataset includes candidate orientations previously presented to users andcandidate orientations previously selected by one or more users forprinting one or more of the plurality of model files; for each of theplurality of candidate orientations: generating one or more geometricattributes for the candidate orientation, each of the geometricattributes representing an aspect of a geometry of the part when thepart is placed in the candidate orientation, and scoring the candidateorientation based on the one or more geometric attributes, the score forthe candidate orientation representing a difficulty of fabricating thepart in the candidate orientation; selecting a subset of the pluralityof candidate orientations, wherein the selection of the candidateorientations is based at least in part on the scores for the candidateorientations; providing the subset of the plurality of candidateorientations for presentation to a user; receiving a rejection by theuser of each candidate orientation of the subset of the plurality ofcandidate orientations presented to the user; selecting, from the subsetof the plurality of candidate orientations, a candidate orientation thatmaximizes a projected area of the part on the build plate and minimizesa number of layers of the part for printing orthogonal to the buildplate; controlling the 3D printer to print the part in the selectedcandidate orientation; and adding the selected candidate orientationselected by the user to the data set.
 2. The method of claim 1, whereinone of the geometric attributes generated for a candidate orientation isan area of the part on the build plate.
 3. The method of claim 1,wherein one of the geometric attributes generated for a candidateorientation is a height of the part in a direction perpendicular to thebuild plate.
 4. The method of claim 1, wherein one of the geometricattributes generated for a candidate orientation is a volume of supportmaterial to be added when fabricating the part in the candidateorientation.
 5. The method of claim 1, wherein one of the geometricattributes generated for a candidate orientation is a value representingan orientation of an intricate feature of the part relative to the buildplate.
 6. The method of claim 1, wherein one of the geometric attributesgenerated for a candidate orientation is an estimated printing time forthe part when fabricating the part in the candidate orientation.
 7. Themethod of claim 1, further comprising determining one or more printsettings based on the selected orientation, the determined printsettings comprising at least one of a print speed, a print quality, alayer thickness, and a material type.
 8. A non-transitorycomputer-readable storage medium having computer-executable instructionsstored thereon, the instructions when executed by a processor causingthe processor to perform operations comprising: obtaining a model filefor a part to be fabricated by a three-dimensional (3D) printer;identifying a plurality of candidate orientations for the part using amachine learning model, each of the candidate orientations representingan orientation of the part on a build plate of the 3D printer, themachine learning model trained on a data set of the candidateorientations for a plurality of model files that were previouslyprinted, wherein the data set includes candidate orientations previouslypresented to one or more users and candidate orientations previouslyselected by one or more users for printing one or more of the pluralityof model files; for each of the plurality of candidate orientations:generating one or more geometric attributes for the candidateorientation, each of the geometric attributes representing an aspect ofa geometry of the part when the part is placed in the candidateorientation, and scoring the candidate orientation based on the one ormore geometric attributes, the score for the candidate orientationrepresenting a difficulty of fabricating the part in the candidateorientation; selecting a subset of the plurality of candidateorientations, wherein the selection of the candidate orientations isbased at least in part on the scores for the candidate orientations;providing the subset of the plurality of candidate orientations forpresentation to a user; receiving a rejection by the user of eachcandidate orientation of the subset of the plurality of candidateorientations presented to the user; selecting, from the subset of theplurality of candidate orientations, a candidate orientation thatmaximizes a projected area of the part on the build plate and minimizesa number of layers of the part for printing orthogonal to the buildplate; controlling the 3D printer to print the part in the selectedcandidate orientation; and adding the selected candidate orientationselected by the user to the data set.
 9. The non-transitorycomputer-readable storage medium of claim 8, wherein one of thegeometric attributes generated for a candidate orientation is an area ofthe part on the build plate.
 10. The non-transitory computer-readablestorage medium of claim 8, wherein one of the geometric attributesgenerated for a candidate orientation is a height of the part in adirection perpendicular to the build plate.
 11. The non-transitorycomputer-readable storage medium of claim 8, wherein one of thegeometric attributes generated for a candidate orientation is a volumeof support material to be added when fabricating the part in thecandidate orientation.
 12. The non-transitory computer-readable storagemedium of claim 8, wherein one of the geometric attributes generated fora candidate orientation is a value representing an orientation of anintricate feature of the part relative to the build plate.
 13. Thenon-transitory computer-readable storage medium of claim 8, wherein oneof the geometric attributes generated for a candidate orientation is anestimated printing time for the part when fabricating the part in thecandidate orientation.
 14. A computing system comprising: a processor;and a non-transitory computer-readable storage medium havingcomputer-executable instructions stored thereon, the instructions whenexecuted by the processor causing the processor to perform operationscomprising: obtaining a model file for a part to be fabricated by athree-dimensional (3D) printer, identifying a plurality of candidateorientations for the part using a machine learning model, each of thecandidate orientations representing an orientation of the part on abuild plate of the 3D printer the machine learning model trained on adata set of candidate orientations for a plurality of model files thatwere previously printed, wherein the data set includes candidateorientations previously presented to users and candidate orientationspreviously selected by one or more users for printing one or more of theplurality of model files, for each of the plurality of candidateorientations: generating one or more geometric attributes for thecandidate orientation, each of the geometric attributes representing anaspect of a geometry of the part when the part is placed in thecandidate orientation, and scoring the candidate orientation based onthe one or more geometric attributes, the score for the candidateorientation representing a difficulty of fabricating the part in thecandidate orientation, selecting a subset of the plurality of candidateorientations, wherein the selection of the candidate orientations isbased at least in part on the scores for the candidate orientations;providing the subset of the plurality of candidate orientations forpresentation to a user; receiving a rejection by the user of eachcandidate orientation of the subset of the plurality of candidateorientations presented to the user; selecting, from the subset of theplurality of candidate orientations, a candidate orientation thatmaximizes a projected area of the part on the build plate and minimizesa number of layers of the part for printing orthogonal to the buildplate; controlling the 3D printer to print the part in the selectedcandidate orientation; and adding the selected candidate orientationselected by the user to the data set.
 15. The method of claim 1, whereinscoring the candidate orientation based on the one or more geometricattributes comprises: determining weights for each of the one or moregeometric attributes using a second machine learning model trained onthe data set, wherein features previously selected by the user areweighted positively; and scoring the candidate orientation based on aweighted combination of the one or more geometric attributes.
 16. Themethod of claim 1, wherein the machine learning model is a neuralnetwork.
 17. The method of claim 1, wherein the machine learning modelis a statistical decision tree.
 18. The method of claim 1, furthercomprising: training the machine learning model on the data setincluding the added orientation.
 19. The non-transitorycomputer-readable storage medium of claim 8, wherein scoring thecandidate orientation based on the one or more geometric attributescomprises: determining weights for each of the one or more geometricattributes using a second machine learning model trained on the dataset, wherein features previously selected by the user are weightedpositively; and scoring the candidate orientation based on a weightedcombination of the one or more geometric attributes.
 20. The computingsystem of claim 14, wherein scoring the candidate orientation based onthe one or more geometric attributes comprises: determining weights foreach of the one or more geometric attributes using a second machinelearning model trained on the data set, wherein features previouslyselected by the user are weighted positively; and scoring the candidateorientation based on a weighted combination of the one or more geometricattributes.